Levelang MCP Server
Integrates the levelang.app translation API with AI assistants to provide translations constrained to specific learner proficiency levels. It supports multiple languages and allows users to control translation styles and moods while dynamically discovering available language configurations.
README
levelang.app MCP Server
An MCP server that exposes the levelang.app translation API to AI assistants. Unlike standard translators that always produce native-speaker complexity, levelang.app constrains translations to the learner's proficiency level.
Features
- Level-Aware Translation — Translate text at beginner, intermediate, advanced, or fluent proficiency with grammar constraints enforced per level
- Multi-Language Support — French, German, Italian, Mandarin Chinese, Cantonese, with transliteration where applicable
- Mood Control — Casual, polite, and formal translation styles
- Language Discovery — Query available languages, levels, and moods dynamically from the backend
- MCP Resources —
levelang://languagesandlevelang://languages/{code}for pulling language configs into context - Stateless Wrapper — No database, no shared state; translates MCP tool calls into backend HTTP requests
Quick Start
You need two things: the MCP server URL and an API key. No local setup required.
Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"levelang": {
"url": "https://your-mcp-server-url/mcp",
"headers": {
"Authorization": "Bearer your-api-key"
}
}
}
}
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"levelang": {
"url": "https://your-mcp-server-url/mcp",
"headers": {
"Authorization": "Bearer your-api-key"
}
}
}
}
Restart Claude Desktop. A hammer icon in the chat input indicates MCP tools are available.
Example Usage
Once connected, ask your AI assistant things like:
Translate "I would like to order a coffee, please" into French at the beginner level.
What languages does Levelang support?
Compare how "I'm worried the new rules might prevent us from finishing on time" translates into German at beginner vs advanced level.
Server Configuration
The settings below are for running the MCP server (local development or self-hosting). End users connecting via URL do not need these.
All configuration is through environment variables. When running locally via stdio, these go in the env block of your MCP client config.
| Variable | Required | Default | Description |
|---|---|---|---|
LEVELANG_API_BASE_URL |
No | http://localhost:8000/api/v1 |
Levelang backend URL |
LEVELANG_API_KEY |
Depends | — | Service key (sk_xxx) for backend auth |
MCP_TRANSPORT |
No | stdio |
Transport: stdio or streamable-http |
MCP_PORT |
No | 8463 |
Port when using HTTP transport |
MCP_API_KEYS |
No | — | Comma-separated valid API keys for HTTP auth |
LEVELANG_API_KEY is required when connecting to a remote backend (staging/production). It may be omitted for local development if the backend has auth disabled.
MCP_API_KEYS controls client authentication for the HTTP transport. When set, clients must send Authorization: Bearer <key> with a key from this list. When empty or unset, auth is disabled (open access). This has no effect on stdio transport.
Local stdio Connection
For local development you can run the MCP server as a subprocess instead of connecting via URL. This requires Python 3.12+, uv, and a running Levelang backend.
git clone https://github.com/beverage/levelang-mcp.git
cd levelang-mcp
uv sync
{
"mcpServers": {
"levelang": {
"command": "uv",
"args": [
"run",
"--directory", "/absolute/path/to/levelang-mcp",
"python", "-m", "levelang_mcp"
],
"env": {
"LEVELANG_API_BASE_URL": "http://localhost:8000/api/v1"
}
}
}
}
Development
Setup
uv sync
git config core.hooksPath .githooks
This enables pre-commit (auto-fix lint + format) and pre-push (lint + format check + tests) hooks.
Running Tests
uv run pytest tests/ -v
MCP Inspector
The MCP Inspector provides a web UI for browsing and invoking tools and resources:
npx @modelcontextprotocol/inspector uv run --directory /path/to/levelang-mcp python -m levelang_mcp
Project Structure
src/levelang_mcp/
├── __main__.py # Entrypoint (python -m levelang_mcp)
├── server.py # MCP tools and resources
├── auth.py # API-key auth middleware for HTTP transport
├── client.py # Async HTTP client for the Levelang API
├── config.py # Environment variable loading
└── formatting.py # API response → human-readable text
tests/
├── test_auth.py # Auth middleware and config tests
├── test_client.py # HTTP client tests (mocked)
├── test_formatting.py
└── test_tools.py # Tool integration tests (mocked)
Architecture
MCP Client levelang-mcp Levelang Backend
(Claude, Cursor, ◄── MCP/stdio ──► (this) ─── HTTP ──► (FastAPI)
Claude Code) POST /translate
GET /languages/details
GET /languages/{code}
The MCP server is a stateless wrapper. It translates MCP tool calls into HTTP requests to the Levelang backend and formats responses as human-readable text for the LLM. It does not share code, database connections, or deployment with the backend.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.